Electrical discharge machining (“EDM”) is used in the manufacture of various metallic components, including for example, gas turbine engine components such as turbine airfoils. EDM uses high energy electrical discharges (i.e., sparks) between an electrode and an electrically conductive workpiece to remove material from the workpiece. The electrode is positioned near the workpiece, separated by only a small distance or gap. A dielectric fluid medium fills the gap between the electrode and the workpiece. A differential voltage of specified magnitude is applied between the electrode and the workpiece. The applied voltage causes the dielectric medium to ionize and break down. Current then starts to flow between the electrode and the workpiece and through the dielectric medium. The current causes heat to be generated at the surface of the workpiece. The heat results in a significant temperature rise and localized melting of the workpiece material. The magnitude of the differential voltage is reduced, the dielectric medium de-ionizes and the current terminates. Heat generation ceases allowing the electrode and the workpiece to cool somewhat. The molten material solidifies as it is flushed from the work area by the dielectric medium, leaving a crater in the workpiece. The crater typically has a shape corresponding with that of the electrode. The workpiece is left with a, shape complementary to that of the electrode. This process, or cycle, commonly referred to as an “on/off” cycle is repeated until material removal is complete.
EDM is particularly useful for machining intricate shapes in electrically conductive materials including cemented carbides and super tough alloys. Typically, these materials are extremely difficult to machine using conventional processes. For example, fragile workpieces can be machined using EDM without undergoing deformations. Conventional finishing operations are generally not required after EDM. One type of EDM process, commonly referred to as wire EDM, uses a small wire to machine an intricate shape into a workpiece. Another type of EDM process, commonly referred to as plunge EDM, uses an elongated electrode that machines recesses or holes in a workpiece. For example, small, deep, shaped holes can be formed in metallic objects such as gas turbine engine components.
Although EDM has been used for a number of years, EDM is a stochastic process and the mechanism, described hereinabove, by which EDM removes metal from a workpiece is complex and still under study. The complexity of the EDM process is due in part to the many factors which have an effect on the outcome of the process. These factors include the process parameters and the machining environmental conditions, together with the properties of the workpiece, the electrode, and the dielectric medium. The adequacy of the process depends on how closely the machining results compare to both the dimensional, geometrical, and functional cut requirements, and the surface finish and hardness requirements.
The important EDM process parameters include peak current, on/off cycle, voltage, polarity and flushing conditions. These parameters typically need to be optimized based on the desired output attribute of the workpiece (e.g., surface finish, recast layer conditions, hardness and stresses). The environmental conditions include temperature and humidity.
The important EDM workpiece and electrode properties include electrical conductivity, melting point, hardness, and dimensional accuracy. A workpiece with a relatively low conductivity results in increased heating and thus a faster rate of material removal. A relatively low melting point of the workpiece results in lower energy requirements for melting. An electrode with a relatively high conductivity and high melting point is, in most cases, ideal for attaining low electrode wear and fast material removal rate.
The properties of the dielectric medium include its dielectric strength, viscosity, and flow rate. The dielectric strength represents the amount of voltage required to ionize the fluid during EDM. A higher dielectric strength results in a lower fluid evaporation rate and a faster ionization/deionization process. The viscosity of the dielectric medium affects its ability to flush away debris and dissipate heat. A relatively low viscosity results in better flushing and more effective heat dissipation. A proper flow rate is needed to remove the molten particles (i.e., debris) away from the work area and to assist in the cooling and cutting processes. Debris in the work area is harmful in that it diverts energy from the workpiece and affects the material removal rate. In addition, debris in the work area can result in arcing, which interrupts the cutting cycle and harms the surface of the workpiece. Further, the presence of such debris makes the process dynamic, random, and difficult to control. This leads to incomplete machining, and damage to the tools and workpiece.
Another factor contributing to the complexity of EDM processes is the complex interaction between the factors above. For example, if the gap dimension is too large, the dielectric medium may never ionize and EDM may never take place. On the other hand, if the gap is too small, the part and the tool may weld together. Generally, a servo-mechanism is used to maintain a constant gap between the electrode and the workpiece. The effectiveness of the servo-mechanism depends on the effectiveness of the EDM parameter selection and optimization, the workpiece/tooling/dielectric fluid variations, and the effectiveness of the equipment maintenance. When the dielectric fluid is contaminated and/or poor flushing conditions exist, undesirable conditions such as arcing, fast electrode wear, and incomplete material removal may occur.
Yet another factor is variability in each of the parameters and properties above. This includes variations that occur during the EDM operation, between successive operations, from one day to the next, and workpiece/electrode/dielectric variation. Some of these are variations with time or use, while others represent supplier and manufacturing tolerances. The properties of the workpiece, dielectric fluid, and tooling are incoming elements that can vary randomly depending on the stability of their manufacturing processes.
Ultimately, such variability can have great significance on the EDM process. Because of its unpredictability, this variability is frequently not accounted for when initially selecting EDM process parameters. Regardless, the variability is often more difficult or impossible to control in a practical manner. The overall quality of the final product often depends on how well the randomly introduced variation and/or uncertainty is dealt with during the off-line adjustments and/or post-processing phase. However, off-line adjustments are typically conducted only after having generated a number of nonconforming parts.
Still further, there are human factors. For example, the process may require an operator to install the workpiece and the electrode in respective fixtures. Any error in the installation can cause mispositioning (in regard to location and/or angle) of the electrode relative to the workpiece. This mispositioning can subsequently result in improper machining.
Due to the inherent stochastic and dynamic nature of EDM processes, it is difficult to create the desired quality in the product at all times. For this reason, post inspection is usually required to check for product quality at the end of the process. The post product inspection phase can be time consuming, costly, subjective, and inaccurate.
To highlight some of the issues above, it is instructive to examine an EDM process for machining cooling passages, i.e., holes, in gas turbine engine components. A typical gas turbine engine has a compressor, a combustor, and a turbine. The compressor and turbine each have a plurality of rotating blades and stationary vanes. The engine operates at high temperatures, often well in excess of about 2750 deg F. (1508 deg C.), for increased performance and efficiency. However, direct exposure to such high temperatures detrimentally affects some turbine components, e.g., blades and vanes, potentially causing component distortion and, in extreme cases, melting.
Cooling techniques have been developed to keep the temperature of the blades and vanes within design limitations, while still operating the engine at high temperatures. For example, blades and vanes exposed to extreme temperatures are typically hollow to permit cooling fluid to flow through them. Furthermore, the outer surface of engine components are typically film cooled with cooling air from the compressor section of the engine. The cooling air typically passes through the component and out a series of small passages or holes (i.e., cooling holes), formed in the outer wall of the component. Film cooling requires less cooling air than other suitable cooling techniques, thereby minimizing the effect on the operating efficiency of the gas turbine engine.
The cooling holes in gas turbine engine components are traditionally created using a complex machining process such as, for example, an EDM process, a laser process, or a combination of the two. Two important characteristics of the cooling hole are break-through and airflow. Break-through is the condition where the cooling passages extend completely through the outer wall of the component. Airflow is a measure that defines the mass flow rate through these passages and may be expressed as a non-dimensional airflow pressure ratio.
Various factors, including each of the factors described above, can influence the quality, e.g., the break-through and airflow characteristics, of the machined passages. One factor is workpiece variation. For example, if the wall thickness varies significantly from part to part, then different cycle times, airflow, and break-through conditions may result. In addition, thicker walls result in more electrode wear and tapering, resulting in passages having a tapering cross sectional opening and undesirably higher airflow characteristics.
Electrode variations also have a direct impact on the final quality attributes of the part. Impurities in the electrodes can cause the grain structure to melt and/or fracture under high temperature conditions. Since the final EDM features reflect the characteristics of the electrode, such defects can lead to incomplete break-through and airflow variation, among others.
Further, use of a contaminated or poor quality dielectric medium has a direct impact on the metal removal rate. A lower medium quality results in a lower metal removal rate and a higher probability for process failure, including incomplete break-through and airflow variation.
Thus, the workpiece must traditionally be manually inspected after machining to determine the break-through and airflow characteristics of the machined holes. Break-through inspection involves manually probing each opening with a pin gage to ensure complete break-through. Airflow inspection involves washing the workpiece, applying wax to certain openings, and airflow testing the non-waxed openings. After inspection, the workpiece is typically heated to remove the wax.
Although a part machined using EDM may have an attribute of high quality without inspection, where the quality of the attribute must be known, the attribute must traditionally be inspected using one or more of the conventional methods described above. However, such manual inspection methods are time consuming, costly, and subject to human error. Consequently, a better method of determining the break-through and airflow characteristics is sought.
Extensive efforts have been directed to developing advanced monitoring systems to facilitate study of EDM voltage and current waveforms to differentiate normal sparks from harmful arc. Other studies have monitored the ignition delay time to study the gap voltage signals. Numerous control systems have been developed to control process parameters. Most of these systems focus in particular on maximizing material removal rate, reducing harmful arcing, and achieving greater process stability. However, none of the aforementioned systems can perform product inspection, predict a characteristic of an attribute, and/or help assure product quality at the end of the process.
U.S. Pat. No. 5,282,261 issued to Skeirik discloses a neural network process measurement and control system. The system uses real time output data from a neural network to replace a sensor or laboratory input to a controller, the network can use readily available measurements from sensors as inputs and produce predicted values of product properties as output. A historical database can be used to provide a history of sensor and laboratory measurements to the neural network. Skeirik discloses that for many products the important product properties relate to the end use of the product and not to the process conditions of the process. However, Skeirik does not disclose a system for use with an EDM process to predict the quality of the attribute produced by the process.
U.S. Pat. No. 5,654,903 issued to Reitman et al. discloses a method and apparatus for monitoring the state of an attribute of a product during the manufacturing process. The system employs an intelligent system trained in the relationship between the signatures of the manufacturing process and the product attribute as a function of time. However, Reitman et al. do not disclose a system for use with an EDM process to predict the quality of the product.
U.S. Pat. No. 5,428,201 issued to Kaneko et al. discloses a method and apparatus for controlling electric discharge machining. Kaneko et al. disclose maintaining the machining gap between the electrode and the workpiece at an essentially constant size based on a reference servo-feed voltage. Fuzzy logic or a neural network are disclosed for calculating the discharge stability. Kaneko et al. disclose that previous methods required the operator to set machining conditions such as on and off times in accordance with requirements such as the machining area of the workpiece, the machining depth, the dimensional accuracy required, and the surface roughness desired. However, this method seeks to control electric discharge machining conditions rather than actually predict product quality.
U.S. Pat. No. 5,571,426 issued to Akemura discloses a method of determining electric discharge machining conditions and an electric discharge machining controller. This method discloses establishing sets of predefined machining parameters, establishing for each set of machining parameters a set of machining data indicating relationships present during EDM among current, depth, electrode undersize, selecting two sets from the sets in accordance with a given set of predefined machining parameters, and inferring and thereupon generating machining condition data comprising machining depth values and corresponding current values for machining the workpiece to a prescribed configuration and prescribed dimensions. However, this method seeks to determine electric discharge machining conditions which minimize the time for roughing operations by reducing the maximum machining current in accordance with the machining depth, rather than actually predict product quality.
It is also known in the art to use a neural network model to predict surface roughness and surface waviness on the basis of pulse-width, the time between two pulses, the wire mechanical tension, and the wire feed speed. See “Study on Modeling of Wire EDM Process”, Spedding, T.A., et al., Journal of Materials Processing Technology, vol. 69, pages 18-28, 1997; and “Parameter Optimization and Surface Characterization of Wire Electrical Discharge Machining Process”, Spedding, T.A., et al., Precision Engineering, vol. 20, pages 5-15, 1997. Such models are suggested for optimization of the process parametric combinations, i.e., selecting target values to achieve a particular result. Other factors (including the workpiece material and dimensions, the cutting voltage, the ignition pulse current, and the dielectric), which may have effects on the measures of the process performance are fixed (held constant). It is suggested that further research might attempt to take more factors, such as wire, workpiece material, and workpiece height, into account as process inputs. However, there is no suggestion that these models can predict product quality.